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Researchers from Salesforce AIResearch have proposed Programmatic VLM Evaluation (PROVE), a new benchmarking paradigm that evaluates VLM responses to open-ended visual queries. Don’t Forget to join our 55k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.
Don’t Forget to join our 50k+ ML SubReddit. Upcoming Live Webinar- Oct 29, 2024] The Best Platform for Serving Fine-Tuned Models: Predibase InferenceEngine (Promoted) The post Google AIResearch Examines Random Circuit Sampling (RCS) for Evaluating Quantum Computer Performance in the Presence of Noise appeared first on MarkTechPost.
SGLang is an open-source inferenceengine designed by the SGLang team to address these challenges. It optimizes CPU and GPU resources during inference, achieving significantly higher throughput than many competitive solutions. Also,feel free to follow us on Twitter and dont forget to join our 75k+ ML SubReddit.
In response, researchers from Salesforce AIResearch introduced BLIP-3-Video, an advanced VLM specifically designed to address the inefficiencies in video processing. Don’t Forget to join our 55k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.
Don’t Forget to join our 50k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.
percentage points on AI benchmark datasets like ARC Challenge and DROP. Compatible with inferenceengines like vLLM and SGLang, allowing flexible deployment on various hardware setups. All credit for this research goes to the researchers of this project. Improves language model training by increasing accuracy by 1.3
Don’t Forget to join our 50k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.
Don’t Forget to join our 50k+ ML SubReddit. Upcoming Live Webinar- Oct 29, 2024] The Best Platform for Serving Fine-Tuned Models: Predibase InferenceEngine (Promoted) The post This AIResearch from Cohere for AI Compares Merging vs Data Mixing as a Recipe for Building High-Performant Aligned LLMs appeared first on MarkTechPost.
The Georgia Institute of Technology and Salesforce AIResearchresearchers introduce a new framework for evaluating RAG systems based on a metric called “sub-question coverage.” Don’t Forget to join our 55k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.
Don’t Forget to join our 50k+ ML SubReddit. Upcoming Live Webinar- Oct 29, 2024] The Best Platform for Serving Fine-Tuned Models: Predibase InferenceEngine (Promoted) The post Google AIResearchers Introduced a Set of New Methods for Enhancing Long-Context LLM Performance in Retrieval-Augmented Generation appeared first on MarkTechPost.
Don’t Forget to join our 55k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.
The team has shared that PowerInfer is a GPU-CPU hybrid inferenceengine that makes use of this understanding. All credit for this research goes to the researchers of this project. It preloads cold-activated neurons onto the CPU for computation and hot-activated neurons onto the GPU for instant access.
Researchers from Meta Fundamental AIResearch (FAIR) have introduced the Open Materials 2024 (OMat24) dataset, which contains over 110 million DFT calculations, making it one of the largest publicly available datasets in this domain. Don’t Forget to join our 50k+ ML SubReddit.
Researchers in AI are working to enable these models to perform not just language understanding but also complex reasoning tasks like problem-solving in mathematics, logic, and general knowledge. In response to these limitations, researchers from Salesforce AIResearch introduced a novel method called ReGenesis.
A major challenge in AIresearch is how to develop models that can balance fast, intuitive reasoning with slower, more detailed reasoning in an efficient way. In AI models, this dichotomy between the two systems mostly presents itself as a trade-off between computational efficiency and accuracy.
Large Language Models (LLMs) have gained significant attention in AIresearch due to their impressive capabilities. Don’t Forget to join our 50k+ ML SubReddit. However, their limitation lies with long-term planning and complex problem-solving. If you like our work, you will love our newsletter.
Model size and inference workloads have grown dramatically as large diffusion models for image production have become more commonplace. Due to resource limitations, optimizing performance for on-device MLinference in mobile contexts is a delicate balancing act. Check Out The Paper and Google AI Article.
This research field is evolving rapidly as AIresearchers explore new methods to enhance LLMs’ capabilities in handling advanced reasoning tasks, particularly in mathematics. Don’t Forget to join our 50k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.
The lack of effective evaluation methods poses a serious problem for AIresearch and development. Current evaluation frameworks, such as LLM-as-a-Judge, which uses large language models to judge outputs from other AI systems, must account for the entire task-solving process. Don’t Forget to join our 50k+ ML SubReddit.
A regular expression inferenceengine that effectively converts regular expressions to finite automata has been designed and implemented. Researchers have achieved competitive GPU utilization and runtimes (seconds) using both shortest path and randomized graph traversals. Check Out The Paper , Github , and CMU Article.
A team of researchers from Microsoft Responsible AIResearch and Johns Hopkins University proposed Controllable Safety Alignment (CoSA) , a framework for efficient inference-time adaptation to diverse safety requirements. Don’t Forget to join our 50k+ ML SubReddit.
Artificial intelligence (AI) and machine learning (ML) revolve around building models capable of learning from data to perform tasks like language processing, image recognition, and making predictions. A significant aspect of AIresearch focuses on neural networks, particularly transformers.
By leveraging DeciCoder alongside Infery LLM, a dedicated inferenceengine, users unlock the power of significantly higher throughput – a staggering 3.5 link] Overall, DeciCoder is more than just a model; it’s a realization of AI efficiency’s potential. The implications of this development are profound.
Meta Lingua’s importance lies in its ability to simplify the experimentation process for NLP researchers. In an era where large language models are at the forefront of AIresearch, having access to a robust yet simple-to-use tool can make all the difference. Don’t Forget to join our 50k+ ML SubReddit.
By leveraging DeciCoder alongside Infery LLM, a dedicated inferenceengine, users unlock the power of significantly higher throughput – a staggering 3.5 link] Overall, DeciCoder is more than just a model; it’s a realization of AI efficiency’s potential. The implications of this development are profound.
Meta AI’s effort to push the boundaries of efficient AI modeling highlights the growing emphasis on sustainable, inclusive AI development—a trend that is sure to shape the future of AIresearch and application. All credit for this research goes to the researchers of this project.
This kind of work is relatively accessible, not requiring insider access to unpublished frontier models, specialized ML experience, or extensive resources. CN’s comments I really enjoyed the entry from an ML perspective and also thought it was very easy to read.
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